Lab12

Laboratory 12: Numpy for Bread! Juans-MacBook-Pro.local juanandreszuluqga /Users/juanandreszuluqga/anaconda3/bin/python 3.11.4 (main, Jul 5 2023, 09:00:44) [Clang 14.0.6 ] sys.version_info(major=3, minor=11, micro=4, releaselevel='final', serial=0) Full name: Juan Zuluaga R#: 11830028 Title of the notebook: Lab 12 Date: 10/04/23 Numpy Numpy is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. The library’s name is short for “Numeric Python” or “Numerical Python”. If you are curious about NumPy, this cheat sheet is recommended: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Numpy_Python_Cheat_Sheet.pdf Arrays A numpy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each dimension. In other words, an array contains information about the raw data, how to locate an element and how to interpret an element.To make a numpy array, you can just use the np.array() function. All you need to do is pass a list to it. Don’t forget that, in order to work with the np.array() function, you need to make sure that the numpy library is present in your environment. If you want to read more about the differences between a Python list and NumPy array, this link is recommended: https://webcourses.ucf.edu/courses/1249560/pages/python-lists-vs-numpy-arrays-what-is-the- difference Example- 1D Arrays Let's create a 1D array from the 2000s (2000-2009): [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009] array([2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]) Example- n-Dimensional Arrays Let's create a 5x2 array from the 2000s (2000-2009): [[2000, 2001], [2002, 2003], [2004, 2005], [2006, 2007], [2008, 2009]] array([[2000, 2001], [2002, 2003], [2004, 2005], [2006, 2007], [2008, 2009]]) Arrays Arithmetic Once you have created the arrays, you can do basic Numpy operations. Numpy offers a variety of operations applicable on arrays. From basic operations such as summation, subtraction, multiplication and division to more advanced and essential operations such as matrix multiplication and other elementwise operations. In the examples below, we will go over some of these: Example- 1D Array Arithmetic Define a 1D array with [0,12,24,36,48,60,72,84,96] Multiple all elements by 2 Take all elements to the power of 2 Find the maximum value of the array and its position Find the minimum value of the array and its position Define another 1D array with [-12,0,12,24,36,48,60,72,84] Find the summation and subtraction of these two arrays Find the multiplication of these two arrays [ 0 12 24 36 48 60 72 84 96] [ 0 24 48 72 96 120 144 168 192] [ 0 144 576 1296 2304 3600 5184 7056 9216] [ 0 144 576 1296 2304 3600 5184 7056 9216] 96 8 0 0 [-12 0 12 24 36 48 60 72 84] [-12 12 36 60 84 108 132 156 180] Example- n-Dimensional Array Arithmetic Define a 2x2 array with [5,10,15,20] Define another 2x2 array with [3,6,9,12] Find the summation and subtraction of these two arrays Find the minimum number in the multiplication of these two arrays Find the position of the maximum in the multiplication of these two arrays Find the mean of the multiplication of these two arrays Find the mean of the first row of the multiplication of these two arrays [[ 5 10] [15 20]] [[ 3 6] [ 9 12]] [[ 8 16] [24 32]] [[105 150] [225 330]] [[105 150] [225 330]] 202.5 127.5 Arrays Comparison Comparing two NumPy arrays determines whether they are equivalent by checking if every element at each corresponding index are the same. Example- 1D Array Comparison Define a 1D array with [1.0,2.5,3.4,7,7] Define another 1D array with [5.0/5.0,5.0/2,6.8/2,21/3,14/2] Compare and see if the two arrays are equal Define another 1D array with [6,1.4,2.2,7.5,7] Compare and see if the first array is greater than or equal to the third array [1. 2.5 3.4 7. 7. ] [1. 2.5 3.4 7. 7. ] [ True True True True True] [6. 1.4 2.2 7.5 7. ] [False True True False True] Arrays Manipulation numpy.copy() allows us to create a copy of an array. This is particularly useful when we need to manipulate an array while keeping an original copy in memory. The numpy.delete() function returns a new array with sub-arrays along an axis deleted. Let's have a look at the examples. Example- Copying and Deleting Arrays and Elements Define a 1D array, named "x" with [1,2,3] Define "y" so that "y=x" Define "z" as a copy of "x" Discuss the difference between y and z Delete the second element of x [1 2 3] [1 2 3] [1 2 3] [1 8 3] [1 8 3] [1 2 3] [1 3] Sorting Arrays Sorting means putting elements in an ordered sequence. Ordered sequence is any sequence that has an order corresponding to elements, like numeric or alphabetical, ascending or descending. If you use the sort() method on a 2-D array, both arrays will be sorted. 10 30 40 20 Original array 10 30 20 40 np . sort(a, axis=0) Sort the array along the first axis 10 20 40 30 np . sort(a) Sort the array along the last axis 10 20 30 40 Sort the flattened array np.sort(a, axis=None) Example- Sorting 1D Arrays Define a 1D array as ['FIFA 2020','Red Dead Redemption','Fallout','GTA','NBA 2018','Need For Speed'] and print it out. Then, sort the array alphabetically. ['FIFA 2020' 'Red Dead Redemption' 'Fallout' 'GTA' 'NBA 2018' 'Need For Speed'] ['FIFA 2020' 'Fallout' 'GTA' 'NBA 2018' 'Need For Speed' 'Red Dead Redemption'] Example- Sorting n-Dimensional Arrays Define a 3x3 array with 17,-6,2,86,-12,0,0,23,12 and print it out. Then, sort the array. [[ 17 -6 2] [ 86 -12 0] [ 0 23 12]] Along columns : [[ 0 -12 0] [ 17 -6 2] [ 86 23 12]] Along rows : [[ -6 2 17] [-12 0 86] [ 0 12 23]] Sorting by default : [[ -6 2 17] [-12 0 86] [ 0 12 23]] Along None Axis : [-12 -6 0 0 2 12 17 23 86] Partitioning (Slice) Arrays Slicing in python means taking elements from one given index to another given index. We can do slicing like this: [start:end]. We can also define the step, like this: [start:end:step]. If we don't pass start its considered 0 If we don't pass end its considered length of array in that dimension If we don't pass step its considered 1 Example- Slicing 1D Arrays Define a 1D array as [1,3,5,7,9], slice out the [3,5,7] and print it out. [1 3 5 7 9] [3 5 7] Example- Slicing n-Dimensional Arrays Define a 5x5 array with "Superman, Batman, Jim Hammond, Captain America, Green Arrow, Aquaman, Wonder Woman, Martian Manhunter, Barry Allen, Hal Jordan, Hawkman, Ray Palmer, Spider Man, Thor, Hank Pym, Solar, Iron Man, Dr. Strange, Daredevil, Ted Kord, Captian Marvel, Black Panther, Wolverine, Booster Gold, Spawn " and print it out. Then: Slice the first column and print it out Slice the third row and print it out Slice 'Wolverine' and print it out Slice a 3x3 array with 'Wonder Woman, Ray Palmer, Iron Man, Martian Manhunter, Spider Man, Dr. Strange, Barry Allen, Thor, Daredevil' [['Superman' 'Batman' 'Jim Hammond' 'Captain America' 'Green Arrow'] ['Aquaman' 'Wonder Woman' 'Martian Manhunter' 'Barry Allen' 'Hal Jordan'] ['Hawkman' 'Ray Palmer' 'Spider Man' 'Thor' 'Hank Pym'] ['Solar' 'Iron Man' 'Dr. Strange' 'Daredevil' 'Ted Kord'] ['Captian Marvel' 'Black Panther' 'Wolverine' 'Booster Gold' 'Spawn']] ['Superman' 'Aquaman' 'Hawkman' 'Solar' 'Captian Marvel'] ['Hawkman' 'Ray Palmer' 'Spider Man' 'Thor' 'Hank Pym'] Wolverine [['Wonder Woman' 'Martian Manhunter' 'Barry Allen'] ['Ray Palmer' 'Spider Man' 'Thor'] ['Iron Man' 'Dr. Strange' 'Daredevil']] This is a Numpy Cheat Sheet- similar to the one you had on top of this notebook! Check out this link for more: https://blog.finxter.com/collection-10-best-numpy-cheat-sheets-every-python-coder-must-own/ Here are some of the resources used for creating this notebook: Johnson, J. (2020). Python Numpy Tutorial (with Jupyter and Colab). Retrieved September 15, 2020, from https://cs231n.github.io/python-numpy-tutorial/ Willems, K. (2019). (Tutorial) Python NUMPY Array TUTORIAL. Retrieved September 15, 2020, from https://www.datacamp.com/community/tutorials/python-numpy-tutorial?utm_source=adwords_ppc Willems, K. (2017). NumPy Cheat Sheet: Data Analysis in Python. Retrieved September 15, 2020, from https://www.datacamp.com/community/blog/python-numpy-cheat-sheet W3resource. (2020). NumPy: Compare two given arrays. Retrieved September 15, 2020, from https://www.w3resource.com/python-exercises/numpy/python-numpy-exercise-28.php Here are some great reads on this topic: "Python NumPy Tutorial" available at * https://www.geeksforgeeks.org/python-numpy-tutorial/ "What Is NumPy?" a collection of blogs, available at * https://realpython.com/tutorials/numpy/ "Look Ma, No For-Loops: Array Programming With NumPy" by Brad Solomon available at * https://realpython.com/numpy-array-programming/ "The Ultimate Beginner’s Guide to NumPy" by Anne Bonner available at * https://towardsdatascience.com/the-ultimate-beginners-guide-to-numpy-f5a2f99aef54 Here are some great videos on these topics: "Learn NUMPY in 5 minutes - BEST Python Library!" by Python Programmer available at * https://www.youtube.com/watch?v=xECXZ3tyONo "Python NumPy Tutorial for Beginners" by freeCodeCamp.org available at * https://www.youtube.com/watch?v=QUT1VHiLmmI "Complete Python NumPy Tutorial (Creating Arrays, Indexing, Math, Statistics, Reshaping)" by Keith Galli available at * https://www.youtube.com/watch?v=GB9ByFAIAH4 "Python NumPy Tutorial | NumPy Array | Python Tutorial For Beginners | Python Training | Edureka" by edureka! available at * https://www.youtube.com/watch?v=8JfDAm9y_7s Exercise: Python List vs. Numpy Arrays? What are some differences between Python lists and Numpy arrays? * Make sure to cite any resources that you may use. Cell In[13], line 29 *Windari, Leonie M. “Difference between Python List and NumPy Array. ^ SyntaxError: invalid character '“' (U+201C) In [1]: # Preamble script block to identify host, user, and kernel import sys ! hostname ! whoami print ( sys . executable ) print ( sys . version ) print ( sys . version_info ) In [2]: import numpy as np #First, we need to impoty "numpy" mylist = [ 2000 , 2001 , 2002 , 2003 , 2004 , 2005 , 2006 , 2007 , 2008 , 2009 ] #Create a list of the years print ( mylist ) #Check how it looks np . array ( mylist ) #Define it as a numpy array Out[2]: In [3]: myotherlist = [[ 2000 , 2001 ],[ 2002 , 2003 ],[ 2004 , 2005 ],[ 2006 , 2007 ],[ 2008 , 2009 ]] #Since I want a 5x2 array, I should group the years two by two print ( myotherlist ) #See how it looks as a list np . array ( myotherlist ) #See how it looks as a numpy array Out[3]: In [4]: import numpy as np #import numpy Array1 = np . array ([ 0 , 12 , 24 , 36 , 48 , 60 , 72 , 84 , 96 ]) #Step1: Define Array1 print ( Array1 ) print ( Array1 * 2 ) #Step2: Multiple all elements by 2 print ( Array1 ** 2 ) #Step3: Take all elements to the power of 2 print ( np . power ( Array1 , 2 )) #Another way to do the same thing, by using a function in numpy print ( np . max ( Array1 )) #Step4: Find the maximum value of the array print ( np . argmax ( Array1 )) ##Step4: Find the postition of the maximum value print ( np . min ( Array1 )) #Step5: Find the minimum value of the array print ( np . argmin ( Array1 )) ##Step5: Find the postition of the minimum value Array2 = np . array ([ - 12 , 0 , 12 , 24 , 36 , 48 , 60 , 72 , 84 ]) #Step6: Define Array2 print ( Array2 ) print ( Array1 + Array2 ) #Step7: Find the summation of these two arrays In [5]: import numpy as np #import numpy Array1 = np . array ([[ 5 , 10 ],[ 15 , 20 ]]) #Step1: Define Array1 print ( Array1 ) Array2 = np . array ([[ 3 , 6 ],[ 9 , 12 ]]) #Step2: Define Array2 print ( Array2 ) print ( Array1 + Array2 ) #Step3: Find the summation MultArray = Array1@Array2 #Step4: To perform a typical matrix multiplication (or matrix product) MultArray1 = Array1 . dot ( Array2 ) #Step4: Another way To perform a matrix multiplication print ( MultArray ) print ( MultArray1 ) print ( np . mean ( MultArray )) ##Step6: Find the mean of the multiplication of these two arrays print ( np . mean ( MultArray [ 0 ,:])) ##Step7: Find the mean of the first row of the multiplication of these two arrays In [6]: import numpy as np #import numpy Array1 = np . array ([ 1.0 , 2.5 , 3.4 , 7 , 7 ]) #Step1: Define Array1 print ( Array1 ) Array2 = np . array ([ 5.0 / 5.0 , 5.0 / 2 , 6.8 / 2 , 21 / 3 , 14 / 2 ]) #Step2: Define Array1 print ( Array2 ) print ( np . equal ( Array1 , Array2 )) #Step3: Compare and see if the two arrays are equal Array3 = np . array ([ 6 , 1.4 , 2.2 , 7.5 , 7 ]) #Step4: Define Array3 print ( Array3 ) print ( np . greater_equal ( Array1 , Array3 )) #Step3: Compare and see if the two arrays are equal In [7]: import numpy as np #import numpy x = np . array ([ 1 , 2 , 3 ]) #Step1: Define x print ( x ) y = x #Step2: Define y as y=x print ( y ) z = np . copy ( x ) #Step3: Define z as a copy of x print ( z ) # For Step4: They look similar but check this out: x [ 1 ] = 8 # If we change x ... print ( x ) print ( y ) print ( z ) # By modifying x, y changes but z remains as a copy of the initial version of x. x = np . delete ( x , 1 ) #Step5: Delete the second element of x print ( x ) In [8]: import numpy as np #import numpy games = np . array ([ 'FIFA 2020' , 'Red Dead Redemption' , 'Fallout' , 'GTA' , 'NBA 2018' , 'Need For Speed' ]) print ( games ) print ( np . sort ( games )) In [9]: import numpy as np #import numpy a = np . array ([[ 17 , - 6 , 2 ],[ 86 , - 12 , 0 ],[ 0 , 23 , 12 ]]) print ( a ) print ( "Along columns : \n" , np . sort ( a , axis = 0 ) ) #This will be sorting in each column print ( "Along rows : \n" , np . sort ( a , axis = 1 ) ) #This will be sorting in each row print ( "Sorting by default : \n" , np . sort ( a ) ) #Same as above print ( "Along None Axis : \n" , np . sort ( a , axis = None ) ) #This will be sorted like a 1D array In [10]: import numpy as np #import numpy a = np . array ([ 1 , 3 , 5 , 7 , 9 ]) #Define the array print ( a ) aslice = a [ 1 : 4 ] #slice the [3,5,7] print ( aslice ) #print it out In [11]: import numpy as np #import numpy Superheroes = np . array ([[ 'Superman' , 'Batman' , 'Jim Hammond' , 'Captain America' , 'Green Arrow' ], [ 'Aquaman' , 'Wonder Woman' , 'Martian Manhunter' , 'Barry Allen' , 'Hal Jordan' ], [ 'Hawkman' , 'Ray Palmer' , 'Spider Man' , 'Thor' , 'Hank Pym' ], [ 'Solar' , 'Iron Man' , 'Dr. Strange' , 'Daredevil' , 'Ted Kord' ], [ 'Captian Marvel' , 'Black Panther' , 'Wolverine' , 'Booster Gold' , 'Spawn' ]]) print ( Superheroes ) #Step1 print ( Superheroes [:, 0 ]) print ( Superheroes [ 2 ,:]) print ( Superheroes [ 4 , 2 ]) print ( Superheroes [ 1 : 4 , 1 : 4 ]) In [13]: Type of Data Consistency : Lists can group various data types ( including text and numbers ) together . NumPy arrays are more tailored for numbers because they demand that every element be of the same type . Performance : For mathematical and numerical tasks , NumPy arrays are faster . For these tasks , lists are slower . Memory Performance : For large datasets in particular , NumPy arrays use memory more effectively . In terms of memory usage , lists can sometimes be less effective . Simple math : Math operations on entire arrays are simple with NumPy . Explicit loops are required for similar operations on lists . Changes in size and shape : For changing the size or shape of data , NumPy arrays are preferable . There are no built -in methods for these changes in lists . integration : Other scholarly libraries can easily be integrated with NumPy . Lists are simpler and more stand - alone . So choose NumPy if you need to work quickly and effectively with a lot of numbers . Python lists may be superior if you want more flexibility and mixed data types . Citations * Windari , Leonie M . “ Difference between Python List and NumPy Array . ” Plainenglish . io / Blog / Python - List - Vs - Numpy - Array - Whats - The - Difference - 7308 cd4b52f6 , 11 July 2021 , plainenglish . io / blog / python - list - vs - numpy - array - whats - the - difference - 7308 cd4b52f6 . Accessed 5 Oct . 2023.